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name: inverse-folding description: "Use ESM-IF1 for fixed-backbone protein inverse design, sequence log-likelihood scoring, multichain conditioning, coordinate extraction, and structure encoder representations." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inverse Folding

Use this sub-skill when a task needs ESM-IF1 to design or score protein sequences from known backbone coordinates. It covers fixed-backbone sequence sampling, single-chain and multichain complex conditioning, PDB/mmCIF coordinate extraction, missing-coordinate handling, conditional log-likelihood scoring, and encoder representations.

When To Use

• Sample candidate amino-acid sequences for a PDB or mmCIF backbone with esm.pretrained.esm_if1_gvp4_t16_142M_UR50() and model.sample(...).
• Score one or more sequences against a structure with score_sequence(...) or score_sequence_in_complex(...).
• Condition target-chain design on all chains in a complex with --multichain-backbone or multichain_util APIs.
• Extract N/CA/C backbone coordinates and native sequences from PDB/mmCIF files.
• Obtain structure-conditioned encoder representations from ESM-IF1.

Route Elsewhere

• Use the sibling structure-prediction sub-skill for ESMFold forward folding from sequence to structure.
• Use the sibling model-embeddings sub-skill for generic protein language-model embeddings that do not use backbone coordinates.
• Use the sibling variant-effect-prediction sub-skill for mutation scoring without a structure-conditioned inverse-folding model.

Start Here

1. Confirm the environment has PyTorch plus inverse-folding optional dependencies, especially torch-geometric and a compatible biotite API.
2. Validate the structure suffix is .pdb or .cif, choose the target chain ID, and decide whether to condition on only the target chain or the whole complex.
3. For CLI-style work, use the bundled dry-run helper in scripts/inverse_folding_cli_helper.py to construct safe commands before running downloads or GPU jobs.
4. For API work, load model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50() and call model.eval() before sampling or scoring.
5. Inspect sampled FASTA output for long amino-acid repeats and inspect scoring CSV output for sequence length mismatches or missing-coordinate effects.

References

• references/workflows.md gives task recipes for sampling, scoring, multichain conditioning, encoder outputs, and diagnostic workflows.
• references/api-reference.md lists the supported ESM-IF1 loader and inverse-folding APIs.
• references/data-formats.md describes coordinate tensors, PDB/mmCIF requirements, FASTA input/output, CSV scoring output, and missing-coordinate conventions.
• references/troubleshooting.md maps common dependency, data, runtime, and biological-quality failures to fixes.

Bundled Helper

Use scripts/inverse_folding_cli_helper.py for self-contained command construction and input validation:

python scripts/inverse_folding_cli_helper.py sample structure.pdb --chain C --outpath designs.fasta
python scripts/inverse_folding_cli_helper.py score structure.pdb variants.fasta --chain C --outpath scores.csv --multichain-backbone

The helper prints a dry-run command by default. Add --execute only when the environment is ready for model loading, potential torch hub downloads, and a long CPU/GPU run.